Recognizing multiclass Static Sign Language words for deaf and dumb people of Bangladesh based on transfer learning techniques
Sign language is a language used for communication of the deaf and dumb (D&D) community. To avoid difficulties in communication among themselves and also among normal people, transfer learning-based automatic sign language recognition can play a great role. Transfer learning has been used in man...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914822002131 |
| _version_ | 1798020705917337600 |
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| author | Md. Monirul Islam Md. Rasel Uddin Md. Nasim AKhtar K.M. Rafiqul Alam |
| author_facet | Md. Monirul Islam Md. Rasel Uddin Md. Nasim AKhtar K.M. Rafiqul Alam |
| author_sort | Md. Monirul Islam |
| collection | DOAJ |
| description | Sign language is a language used for communication of the deaf and dumb (D&D) community. To avoid difficulties in communication among themselves and also among normal people, transfer learning-based automatic sign language recognition can play a great role. Transfer learning has been used in many countries in sign language. In Bangladesh, here also some techniques consisting of a convolutional neural network and transfer learning have been used to recognize Bangladeshi Sign language. Those techniques have been used only to sign alphabets, characters, and numbers. In this paper, a transfer learning based automatic sign language recognition system is introduced using Bangladeshi Sign Language (BdSL) words. Very rare research has been done on Bangladeshi Sign Words, and there is an inadequate Bangladeshi Sign Words dataset. This system employs four well-performed transfer learning techniques named VGG16, VGG19, AlexNet and InceptionV3 with pre-trained weights. The accuracy, recall, precision, and F1 score are used to assess the efficiency of the suggested models. The dataset of Bangladeshi sign words has been used in this paper which is consisting of 1105 images. The models show the training accuracy of 99.92%, 99.58%, 98.70% and 97.86% for VGG16, VGG19, InceptionV3 and AlexNet respectively whereas validation accuracy is 92.41%, 91.62%, 88.22% and 84.95% for VGG16, VGG19, InceptionV3 and AlexNet respectively. The proposed transfer learning based on the CNN method demonstrates better performance for the recognition of Bangladeshi Sign Words. |
| first_indexed | 2024-04-11T17:01:57Z |
| format | Article |
| id | doaj.art-e77b298c67a743fba8404938fb4436b1 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-04-11T17:01:57Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-e77b298c67a743fba8404938fb4436b12022-12-22T04:13:08ZengElsevierInformatics in Medicine Unlocked2352-91482022-01-0133101077Recognizing multiclass Static Sign Language words for deaf and dumb people of Bangladesh based on transfer learning techniquesMd. Monirul Islam0Md. Rasel Uddin1Md. Nasim AKhtar2K.M. Rafiqul Alam3Department of Computer Science and Engineering, University of Information Technology and Sciences (UITS), Dhaka 1212, Bangladesh; Corresponding author.Department of Computer Science and Engineering, University of Information Technology and Sciences (UITS), Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, Dhaka University of Engineering and Technology, Gazipur 1707, BangladeshDepartment of Statistics, Jahangirnagar University, Dhaka 1342, BangladeshSign language is a language used for communication of the deaf and dumb (D&D) community. To avoid difficulties in communication among themselves and also among normal people, transfer learning-based automatic sign language recognition can play a great role. Transfer learning has been used in many countries in sign language. In Bangladesh, here also some techniques consisting of a convolutional neural network and transfer learning have been used to recognize Bangladeshi Sign language. Those techniques have been used only to sign alphabets, characters, and numbers. In this paper, a transfer learning based automatic sign language recognition system is introduced using Bangladeshi Sign Language (BdSL) words. Very rare research has been done on Bangladeshi Sign Words, and there is an inadequate Bangladeshi Sign Words dataset. This system employs four well-performed transfer learning techniques named VGG16, VGG19, AlexNet and InceptionV3 with pre-trained weights. The accuracy, recall, precision, and F1 score are used to assess the efficiency of the suggested models. The dataset of Bangladeshi sign words has been used in this paper which is consisting of 1105 images. The models show the training accuracy of 99.92%, 99.58%, 98.70% and 97.86% for VGG16, VGG19, InceptionV3 and AlexNet respectively whereas validation accuracy is 92.41%, 91.62%, 88.22% and 84.95% for VGG16, VGG19, InceptionV3 and AlexNet respectively. The proposed transfer learning based on the CNN method demonstrates better performance for the recognition of Bangladeshi Sign Words.http://www.sciencedirect.com/science/article/pii/S2352914822002131Transfer learningVGG16VGG19InceptionV3AlexNetBengali Sign Word |
| spellingShingle | Md. Monirul Islam Md. Rasel Uddin Md. Nasim AKhtar K.M. Rafiqul Alam Recognizing multiclass Static Sign Language words for deaf and dumb people of Bangladesh based on transfer learning techniques Informatics in Medicine Unlocked Transfer learning VGG16 VGG19 InceptionV3 AlexNet Bengali Sign Word |
| title | Recognizing multiclass Static Sign Language words for deaf and dumb people of Bangladesh based on transfer learning techniques |
| title_full | Recognizing multiclass Static Sign Language words for deaf and dumb people of Bangladesh based on transfer learning techniques |
| title_fullStr | Recognizing multiclass Static Sign Language words for deaf and dumb people of Bangladesh based on transfer learning techniques |
| title_full_unstemmed | Recognizing multiclass Static Sign Language words for deaf and dumb people of Bangladesh based on transfer learning techniques |
| title_short | Recognizing multiclass Static Sign Language words for deaf and dumb people of Bangladesh based on transfer learning techniques |
| title_sort | recognizing multiclass static sign language words for deaf and dumb people of bangladesh based on transfer learning techniques |
| topic | Transfer learning VGG16 VGG19 InceptionV3 AlexNet Bengali Sign Word |
| url | http://www.sciencedirect.com/science/article/pii/S2352914822002131 |
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